Artificial neural network based predictions of cetane number for furanic biofuel additives

•An artificial neural network is used to accurately predict the cetane number of molecules.•A methodology for extending predictions to underrepresented classes is demonstrated.•Model predictions are compared to literature values where applicable.•Two of the furanic candidates posses CN’s in a suitable range for use in traditional diesel engines.